向量标准化预测 (VectorStandardScalerPredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.dataproc.vector.VectorStandardScalerPredictStreamOp

Python 类名：VectorStandardScalerPredictStreamOp

功能介绍

标准化是对向量数据进行按正态化处理的组件

加载VectorStandardScalerTrainBatchOp中生成的模型，对向量数据做标准化预处理。

参数说明

名称	中文名称	描述	类型	是否必须？	取值范围	默认值
modelFilePath	模型的文件路径	模型的文件路径	String			null
outputCol	输出结果列	输出结果列列名，可选，默认null	String			null
numThreads	组件多线程线程个数	组件多线程线程个数	Integer			1
modelStreamFilePath	模型流的文件路径	模型流的文件路径	String			null
modelStreamScanInterval	扫描模型路径的时间间隔	描模型路径的时间间隔，单位秒	Integer			10
modelStreamStartTime	模型流的起始时间	模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式，详见Timestamp.valueOf(String s)	String			null

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
    ["a", "10.0, 100"],
    ["b", "-2.5, 9"],
    ["c", "100.2, 1"],
    ["d", "-99.9, 100"],
    ["a", "1.4, 1"],
    ["b", "-2.2, 9"],
    ["c", "100.9, 1"]
])

data = BatchOperator.fromDataframe(df, schemaStr="col1 string, vec string")
colnames = ["col1", "vec"]
selectedColName = "vec"

trainOp = VectorStandardScalerTrainBatchOp()\
           .setSelectedCol(selectedColName)

model = trainOp.linkFrom(data)

#batch predict
batchPredictOp = VectorStandardScalerPredictBatchOp()
batchPredictOp.linkFrom(model, data).print()

#stream predict
streamData = StreamOperator.fromDataframe(df, schemaStr="col1 string, vec string")

streamPredictOp = VectorStandardScalerPredictStreamOp(trainOp)
streamData.link(streamPredictOp).print()

StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.vector.VectorStandardScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.vector.VectorStandardScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.vector.VectorStandardScalerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;

public class VectorStandardScalerPredictStreamOpTest {
	@Test
	public void testVectorStandardScalerPredictStreamOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of("a", "10.0, 100"),
			Row.of("b", "-2.5, 9"),
			Row.of("c", "100.2, 1"),
			Row.of("d", "-99.9, 100"),
			Row.of("a", "1.4, 1"),
			Row.of("b", "-2.2, 9"),
			Row.of("c", "100.9, 1")
		);
		BatchOperator <?> data = new MemSourceBatchOp(df, "col1 string, vec string");
		BatchOperator <?> trainOp = new VectorStandardScalerTrainBatchOp()
			.setSelectedCol("vec");
		BatchOperator <?> model = trainOp.linkFrom(data);
		BatchOperator <?> batchPredictOp = new VectorStandardScalerPredictBatchOp();
		batchPredictOp.linkFrom(model, data).print();
		StreamOperator <?> streamData = new MemSourceStreamOp(df, "col1 string, vec string");
		StreamOperator <?> streamPredictOp = new VectorStandardScalerPredictStreamOp(trainOp);
		streamData.link(streamPredictOp).print();
		StreamOperator.execute();
	}
}

运行结果

col1	vec
a	-0.07835182408093559,1.4595814453461897
c	1.2269606224811418,-0.6520885789229323
b	-0.2549018445693762,-0.4814485769617911
a	-0.20280511721213143,-0.6520885789229323
c	1.237090541689495,-0.6520885789229323
b	-0.25924323851581327,-0.4814485769617911
d	-1.6687491397923802,1.4595814453461897